Environment and/or Target Segmentation

ABSTRACT

A depth image of a scene may be observed or captured by a capture device. The depth image may include a human target and an environment. One or more pixels of the depth image may be analyzed to determine whether the pixels in the depth image are associated with the environment of the depth image. The one or more pixels associated with the environment may then be discarded to isolate the human target and the depth image with the isolated human target may be processed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/475,094 filed on May 29, 2009, the entire contents of which isincorporated herein by reference.

BACKGROUND

Many computing applications such as computer games, multimediaapplications, or the like use controls to allow users to manipulate gamecharacters or other aspects of an application. Typically such controlsare input using, for example, controllers, remotes, keyboards, mice, orthe like. Unfortunately, such controls can be difficult to learn, thuscreating a barrier between a user and such games and applications.Furthermore, such controls may be different than actual game actions orother application actions for which the controls are used. For example,a game control that causes a game character to swing a baseball bat maynot correspond to an actual motion of swinging the baseball bat.

SUMMARY

Disclosed herein are systems and methods for processing depthinformation of a scene that may be used to interpret human input. Forexample, a depth image of the scene may be received, captured, orobserved. The depth image may include a human target and an environmentsuch as a background, one or more non-human target foreground object, orthe like. According to an example embodiment, the depth image may beanalyzed to determine one or more pixels associated with the humantarget and the environment such as the pixels that may not be associatedwith the human target, or the non-player pixels. The one or more pixelsassociated with the environment may then be removed from the depth imagesuch that the human target may be isolated in the depth image. Theisolated human target may be used to track a model of human target to,for example, animate an avatar and/or control various computingapplications.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate an example embodiment of a targetrecognition, analysis, and tracking system with a user playing a game.

FIG. 2 illustrates an example embodiment of a capture device that may beused in a target recognition, analysis, and tracking system.

FIG. 3 illustrates an example embodiment of a computing environment thatmay be used to interpret one or more gestures in a target recognition,analysis, and tracking system.

FIG. 4 illustrates another example embodiment of a computing environmentthat may be used to interpret one or more gestures in a targetrecognition, analysis, and tracking system.

FIG. 5 depicts a flow diagram of an example method for segmenting ahuman target from an environment in a depth image.

FIG. 6A illustrates an example embodiment of a depth image that may bereceived.

FIG. 6B illustrates an example embodiment of the depth image illustratedin FIG. 6A with a human target segmented or separated from anenvironment of the depth image.

FIG. 7 illustrates an example embodiment of a depth image that mayinclude an infrared shadow.

FIG. 8 illustrates an example embodiment of a depth image with abounding box that may be defined around a human target.

FIG. 9A illustrates an example embodiment of a depth image with a bodypart of a human target isolated.

FIG. 9B illustrates an example embodiment of the depth image of FIG. 9Awith the human target segmented from an environment.

FIG. 10 illustrates an example embodiment of depth history dataassociated with depth images.

FIG. 11A depicts an example embodiment of a depth image that may becaptured.

FIG. 11B illustrates an example embodiment of depth history dataincluding maximum depth values accumulated over time.

FIG. 11C illustrates an example embodiment of a depth image with a humantarget segmented from an environment.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

As will be described herein, a user may control an application executingon a computing environment such as a game console, a computer, or thelike by performing one or more gestures. According to one embodiment,the gestures may be received by, for example, a capture device. Forexample, the capture device may capture a depth image of a scene. In oneembodiment, the depth image of the scene may be received, captured, orobserved. The depth image may include a human target and an environmentsuch as a background, foreground objects that may not be associated withthe human target, or the like. In an example embodiment, the environmentmay include one or more non-human targets such as a wall, furniture, orthe like. The depth image may be analyzed to determine whether one ormore pixels are associated with the environment and the human target.The one or more pixels associated with the environment may be removed ordiscarded to isolate the foreground object. The depth image with theisolated foreground object may then be processed. For example, asdescribed above, the isolated foreground object may include a humantarget. According to an example embodiment, a model of human target, orany other desired shape may be generated and/or tracked to, for example,animate an avatar and/or control various computing applications.

FIGS. 1A and 1B illustrate an example embodiment of a configuration of atarget recognition, analysis, and tracking system 10 with a user 18playing a boxing game. In an example embodiment, the target recognition,analysis, and tracking system 10 may be used to recognize, analyze,and/or track a human target such as the user 18.

As shown in FIG. 1A, the target recognition, analysis, and trackingsystem 10 may include a computing environment 12. The computingenvironment 12 may be a computer, a gaming system or console, or thelike. According to an example embodiment, the computing environment 12may include hardware components and/or software components such that thecomputing environment 12 may be used to execute applications such asgaming applications, non-gaming applications, or the like. In oneembodiment, the computing environment 12 may include a processor such asa standardized processor, a specialized processor, a microprocessor, orthe like that may execute instructions including, for example,instructions for receiving a depth image of a scene, determining whetherone or more pixels are associated with an environment of the depthimage, discarding the one or more pixels associated with the environmentfrom the depth image to isolate a desired object such as a human targetin the depth image, processing the depth image with the isolated desiredobject, which will be described in more detail below.

As shown in FIG. 1A, the target recognition, analysis, and trackingsystem 10 may further include a capture device 20. The capture device 20may be, for example, a camera that may be used to visually monitor oneor more users, such as the user 18, such that gestures and/or movementsperformed by the one or more users may be captured, analyzed, andtracked to perform one or more controls or actions within an applicationand/or animate an avatar or on-screen character.

According to one embodiment, the target recognition, analysis, andtracking system 10 may be connected to an audiovisual device 16 such asa television, a monitor, a high-definition television (HDTV), or thelike that may provide game or application visuals and/or audio to a usersuch as the user 18. For example, the computing environment 12 mayinclude a video adapter such as a graphics card and/or an audio adaptersuch as a sound card that may provide audiovisual signals associatedwith the game application, non-game application, or the like. Theaudiovisual device 16 may receive the audiovisual signals from thecomputing environment 12 and may then output the game or applicationvisuals and/or audio associated with the audiovisual signals to the user18. According to one embodiment, the audiovisual device 16 may beconnected to the computing environment 12 via, for example, an S-Videocable, a coaxial cable, an HDMI cable, a DVI cable, a VGA cable, or thelike.

As shown in FIGS. 1A and 1B, the target recognition, analysis, andtracking system 10 may be used to recognize, analyze, and/or track ahuman target such as the user 18. For example, the user 18 may betracked using the capture device 20 such that the movements of user 18may be interpreted as controls that may be used to affect theapplication being executed by computer environment 12. Thus, accordingto one embodiment, the user 18 may move his or her body to control theapplication.

As shown in FIGS. 1A and 1B, in an example embodiment, the applicationexecuting on the computing environment 12 may be a boxing game that theuser 18 may be playing. For example, the computing environment 12 mayuse the audiovisual device 16 to provide a visual representation of aboxing opponent 38 to the user 18. The computing environment 12 may alsouse the audiovisual device 16 to provide a visual representation of ahuman target avatar 40 that the user 18 may control with his or hermovements. For example, as shown in FIG. 1B, the user 18 may throw apunch in physical space to cause the human target avatar 40 to throw apunch in game space. Thus, according to an example embodiment, thecomputer environment 12 and the capture device 20 of the targetrecognition, analysis, and tracking system 10 may be used to recognizeand analyze the punch of the user 18 in physical space such that thepunch may be interpreted as a game control of the human target avatar 40in game space.

Other movements by the user 18 may also be interpreted as other controlsor actions, such as controls to bob, weave, shuffle, block, jab, orthrow a variety of different power punches. Furthermore, some movementsmay be interpreted as controls that may correspond to actions other thancontrolling the human target avatar 40. For example, the human targetmay use movements to end, pause, or save a game, select a level, viewhigh scores, communicate with a friend, etc. Additionally, a full rangeof motion of the user 18 may be available, used, and analyzed in anysuitable manner to interact with an application.

In example embodiments, the human target such as the user 18 may have anobject. In such embodiments, the user of an electronic game may beholding the object such that the motions of the human target and theobject may be used to adjust and/or control parameters of the game. Forexample, the motion of a human target holding a racket may be trackedand utilized for controlling an on-screen racket in an electronic sportsgame. In another example embodiment, the motion of a human targetholding an object may be tracked and utilized for controlling anon-screen weapon in an electronic combat game.

According to other example embodiments, the target recognition,analysis, and tracking system 10 may further be used to interpret targetmovements as operating system and/or application controls that areoutside the realm of games. For example, virtually any controllableaspect of an operating system and/or application may be controlled bymovements of the target such as the user 18.

FIG. 2 illustrates an example embodiment of the capture device 20 thatmay be used in the target recognition, analysis, and tracking system 10.According to an example embodiment, the capture device 20 may beconfigured to capture video with depth information including a depthimage that may include depth values via any suitable techniqueincluding, for example, time-of-flight, structured light, stereo image,or the like. According to one embodiment, the capture device 20 mayorganize the calculated depth information into “Z layers,” or layersthat may be perpendicular to a Z axis extending from the depth cameraalong its line of sight.

As shown in FIG. 2, the capture device 20 may include an image cameracomponent 22. According to an example embodiment, the image cameracomponent 22 may be a depth camera that may capture the depth image of ascene. The depth image may include a two-dimensional (2-D) pixel area ofthe captured scene where each pixel in the 2-D pixel area may representa depth value such as a length or distance in, for example, centimeters,millimeters, or the like of an object in the captured scene from thecamera.

As shown in FIG. 2, according to an example embodiment, the image cameracomponent 22 may include an IR light component 24, a three-dimensional(3-D) camera 26, and an RGB camera 28 that may be used to capture thedepth image of a scene. For example, in time-of-flight analysis, the IRlight component 24 of the capture device 20 may emit an infrared lightonto the scene and may then use sensors (not shown) to detect thebackscattered light from the surface of one or more targets and objectsin the scene using, for example, the 3-D camera 26 and/or the RGB camera28. In some embodiments, pulsed infrared light may be used such that thetime between an outgoing light pulse and a corresponding incoming lightpulse may be measured and used to determine a physical distance from thecapture device 20 to a particular location on the targets or objects inthe scene. Additionally, in other example embodiments, the phase of theoutgoing light wave may be compared to the phase of the incoming lightwave to determine a phase shift. The phase shift may then be used todetermine a physical distance from the capture device to a particularlocation on the targets or objects.

According to another example embodiment, time-of-flight analysis may beused to indirectly determine a physical distance from the capture device20 to a particular location on the targets or objects by analyzing theintensity of the reflected beam of light over time via varioustechniques including, for example, shuttered light pulse imaging.

In another example embodiment, the capture device 20 may use astructured light to capture depth information. In such an analysis,patterned light (i.e., light displayed as a known pattern such as gridpattern or a stripe pattern) may be projected onto the scene via, forexample, the IR light component 24. Upon striking the surface of one ormore targets or objects in the scene, the pattern may become deformed inresponse. Such a deformation of the pattern may be captured by, forexample, the 3-D camera 26 and/or the RGB camera 28 and may then beanalyzed to determine a physical distance from the capture device 20 toa particular location on the targets or objects.

According to another embodiment, the capture device 20 may include twoor more physically separated cameras that may view a scene fromdifferent angles, to obtain visual stereo data that may be resolved togenerate depth information.

The capture device 20 may further include a microphone 30. Themicrophone 30 may include a transducer or sensor that may receive andconvert sound into an electrical signal. According to one embodiment,the microphone 30 may be used to reduce feedback between the capturedevice 20 and the computing environment 12 in the target recognition,analysis, and tracking system 10. Additionally, the microphone 30 may beused to receive audio signals that may also be provided by the user tocontrol applications such as game applications, non-game applications,or the like that may be executed by the computing environment 12.

In an example embodiment, the capture device 20 may further include aprocessor 32 that may be in operative communication with the imagecamera component 22. The processor 32 may include a standardizedprocessor, a specialized processor, a microprocessor, or the like thatmay execute instructions that may include instructions for receiving adepth image of a scene, determining whether one or more pixelsassociated with an environment of the depth image, discarding the one ormore pixels associated with the environment from the depth image toisolate a desired object such as a human target in the depth image,processing the depth image with the isolated desired object, which willbe described in more detail below.

The capture device 20 may further include a memory component 34 that maystore the instructions that may be executed by the processor 32, imagesor frames of images captured by the 3-D camera or RGB camera, or anyother suitable information, images, or the like. According to an exampleembodiment, the memory component 34 may include random access memory(RAM), read only memory (ROM), cache, Flash memory, a hard disk, or anyother suitable storage component. As shown in FIG. 2, in one embodiment,the memory component 34 may be a separate component in communicationwith the image capture component 22 and the processor 32. According toanother embodiment, the memory component 34 may be integrated into theprocessor 32 and/or the image capture component 22.

As shown in FIG. 2, the capture device 20 may be in communication withthe computing environment 12 via a communication link 36. Thecommunication link 36 may be a wired connection including, for example,a USB connection, a Firewire connection, an Ethernet cable connection,or the like and/or a wireless connection such as a wireless 802.11b, g,a, or n connection. According to one embodiment, the computingenvironment 12 may provide a clock to the capture device 20 that may beused to determine when to capture, for example, a scene via thecommunication link 36.

Additionally, the capture device 20 may provide the depth informationand images captured by, for example, the 3-D camera 26 and/or the RGBcamera 28, and a skeletal model that may be generated by the capturedevice 20 to the computing environment 12 via the communication link 36.The computing environment 12 may then use the skeletal model, depthinformation, and captured images to, for example, control an applicationsuch as a game or word processor. For example, as shown, in FIG. 2, thecomputing environment 12 may include a gestures library 190. Thegestures library 190 may include a collection of gesture filters, eachcomprising information concerning a gesture that may be performed by theskeletal model (as the user moves). The data captured by the cameras 26,28 and device 20 in the form of the skeletal model and movementsassociated with it may be compared to the gesture filters in the gesturelibrary 190 to identify when a user (as represented by the skeletalmodel) has performed one or more gestures. Those gestures may beassociated with various controls of an application. Thus, the computingenvironment 12 may use the gestures library 190 to interpret movementsof the skeletal model and to control an application based on themovements.

FIG. 3 illustrates an example embodiment of a computing environment thatmay be used to interpret one or more gestures in a target recognition,analysis, and tracking system. The computing environment such as thecomputing environment 12 described above with respect to FIGS. 1A-2 maybe a multimedia console 100, such as a gaming console. As shown in FIG.3, the multimedia console 100 has a central processing unit (CPU) 101having a level 1 cache 102, a level 2 cache 104, and a flash ROM (ReadOnly Memory) 106. The level 1 cache 102 and a level 2 cache 104temporarily store data and hence reduce the number of memory accesscycles, thereby improving processing speed and throughput. The CPU 101may be provided having more than one core, and thus, additional level 1and level 2 caches 102 and 104. The flash ROM 106 may store executablecode that is loaded during an initial phase of a boot process when themultimedia console 100 is powered ON.

A graphics processing unit (GPU) 108 and a video encoder/video codec(coder/decoder) 114 form a video processing pipeline for high speed andhigh resolution graphics processing. Data is carried from the graphicsprocessing unit 108 to the video encoder/video codec 114 via a bus. Thevideo processing pipeline outputs data to an A/V (audio/video) port 140for transmission to a television or other display. A memory controller110 is connected to the GPU 108 to facilitate processor access tovarious types of memory 112, such as, but not limited to, a RAM (RandomAccess Memory).

The multimedia console 100 includes an I/O controller 120, a systemmanagement controller 122, an audio processing unit 123, a networkinterface controller 124, a first USB host controller 126, a second USBcontroller 128 and a front panel I/O subassembly 130 that are preferablyimplemented on a module 118. The USB controllers 126 and 128 serve ashosts for peripheral controllers 142(1)-142(2), a wireless adapter 148,and an external memory device 146 (e.g., flash memory, external CD/DVDROM drive, removable media, etc.). The network interface 124 and/orwireless adapter 148 provide access to a network (e.g., the Internet,home network, etc.) and may be any of a wide variety of various wired orwireless adapter components including an Ethernet card, a modem, aBluetooth module, a cable modem, and the like.

System memory 143 is provided to store application data that is loadedduring the boot process. A media drive 144 is provided and may comprisea DVD/CD drive, hard drive, or other removable media drive, etc. Themedia drive 144 may be internal or external to the multimedia console100. Application data may be accessed via the media drive 144 forexecution, playback, etc. by the multimedia console 100. The media drive144 is connected to the I/O controller 120 via a bus, such as a SerialATA bus or other high speed connection (e.g., IEEE 1394).

The system management controller 122 provides a variety of servicefunctions related to assuring availability of the multimedia console100. The audio processing unit 123 and an audio codec 132 form acorresponding audio processing pipeline with high fidelity and stereoprocessing. Audio data is carried between the audio processing unit 123and the audio codec 132 via a communication link. The audio processingpipeline outputs data to the A/V port 140 for reproduction by anexternal audio human target or device having audio capabilities.

The front panel I/O subassembly 130 supports the functionality of thepower button 150 and the eject button 152, as well as any LEDs (lightemitting diodes) or other indicators exposed on the outer surface of themultimedia console 100. A system power supply module 136 provides powerto the components of the multimedia console 100. A fan 138 cools thecircuitry within the multimedia console 100.

The CPU 101, GPU 108, memory controller 110, and various othercomponents within the multimedia console 100 are interconnected via oneor more buses, including serial and parallel buses, a memory bus, aperipheral bus, and a processor or local bus using any of a variety ofbus architectures. By way of example, such architectures can include aPeripheral Component Interconnects (PCI) bus, PCI-Express bus, etc.

When the multimedia console 100 is powered ON, application data may beloaded from the system memory 143 into memory 112 and/or caches 102, 104and executed on the CPU 101. The application may present a graphicaluser interface that provides a consistent user experience whennavigating to different media types available on the multimedia console100. In operation, applications and/or other media contained within themedia drive 144 may be launched or played from the media drive 144 toprovide additional functionalities to the multimedia console 100.

The multimedia console 100 may be operated as a standalone system bysimply connecting the system to a television or other display. In thisstandalone mode, the multimedia console 100 allows one or more users tointeract with the system, watch movies, or listen to music. However,with the integration of broadband connectivity made available throughthe network interface 124 or the wireless adapter 148, the multimediaconsole 100 may further be operated as a participant in a larger networkcommunity.

When the multimedia console 100 is powered ON, a set amount of hardwareresources are reserved for system use by the multimedia consoleoperating system. These resources may include a reservation of memory(e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking bandwidth(e.g., 8 kbs), etc. Because these resources are reserved at system boottime, the reserved resources do not exist from the application's view.

In particular, the memory reservation preferably is large enough tocontain the launch kernel, concurrent system applications and drivers.The CPU reservation is preferably constant such that if the reserved CPUusage is not used by the system applications, an idle thread willconsume any unused cycles.

With regard to the GPU reservation, lightweight messages generated bythe system applications (e.g., popups) are displayed by using a GPUinterrupt to schedule code to render popup into an overlay. The amountof memory required for an overlay depends on the overlay area size andthe overlay preferably scales with screen resolution. Where a full userinterface is used by the concurrent system application, it is preferableto use a resolution independent of application resolution. A scaler maybe used to set this resolution such that the need to change frequencyand cause a TV resynch is eliminated.

After the multimedia console 100 boots and system resources arereserved, concurrent system applications execute to provide systemfunctionalities. The system functionalities are encapsulated in a set ofsystem applications that execute within the reserved system resourcesdescribed above. The operating system kernel identifies threads that aresystem application threads versus gaming application threads. The systemapplications are preferably scheduled to run on the CPU 101 atpredetermined times and intervals in order to provide a consistentsystem resource view to the application. The scheduling is to minimizecache disruption for the gaming application running on the console.

When a concurrent system application requires audio, audio processing isscheduled asynchronously to the gaming application due to timesensitivity. A multimedia console application manager (described below)controls the gaming application audio level (e.g., mute, attenuate) whensystem applications are active.

Input devices (e.g., controllers 142(1) and 142(2)) are shared by gamingapplications and system applications. The input devices are not reservedresources, but are to be switched between system applications and thegaming application such that each will have a focus of the device. Theapplication manager preferably controls the switching of input stream,without knowledge the gaming application's knowledge and a drivermaintains state information regarding focus switches. The cameras 26, 28and capture device 20 may define additional input devices for theconsole 100.

FIG. 4 illustrates another example embodiment of a computing environment220 that may be the computing environment 12 shown in FIGS. 1A-2 used tointerpret one or more gestures in a target recognition, analysis, andtracking system. The computing system environment 220 is only oneexample of a suitable computing environment and is not intended tosuggest any limitation as to the scope of use or functionality of thepresently disclosed subject matter. Neither should the computingenvironment 220 be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary operating environment 220. In some embodiments the variousdepicted computing elements may include circuitry configured toinstantiate specific aspects of the present disclosure. For example, theterm circuitry used in the disclosure can include specialized hardwarecomponents configured to perform function(s) by firmware or switches. Inother examples embodiments the term circuitry can include a generalpurpose processing unit, memory, etc., configured by softwareinstructions that embody logic operable to perform function(s). Inexample embodiments where circuitry includes a combination of hardwareand software, an implementer may write source code embodying logic andthe source code can be compiled into machine readable code that can beprocessed by the general purpose processing unit. Since one skilled inthe art can appreciate that the state of the art has evolved to a pointwhere there is little difference between hardware, software, or acombination of hardware/software, the selection of hardware versussoftware to effectuate specific functions is a design choice left to animplementer. More specifically, one of skill in the art can appreciatethat a software process can be transformed into an equivalent hardwarestructure, and a hardware structure can itself be transformed into anequivalent software process. Thus, the selection of a hardwareimplementation versus a software implementation is one of design choiceand left to the implementer.

In FIG. 4, the computing environment 220 comprises a computer 241, whichtypically includes a variety of computer readable media. Computerreadable media can be any available media that can be accessed bycomputer 241 and includes both volatile and nonvolatile media, removableand non-removable media. The system memory 222 includes computer storagemedia in the form of volatile and/or nonvolatile memory such as readonly memory (ROM) 223 and random access memory (RAM) 260. A basicinput/output system 224 (BIOS), containing the basic routines that helpto transfer information between elements within computer 241, such asduring start-up, is typically stored in ROM 223. RAM 260 typicallycontains data and/or program modules that are immediately accessible toand/or presently being operated on by processing unit 259. By way ofexample, and not limitation, FIG. 4 illustrates operating system 225,application programs 226, other program modules 227, and program data228.

The computer 241 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 4 illustrates a hard disk drive 238 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 239that reads from or writes to a removable, nonvolatile magnetic disk 254,and an optical disk drive 240 that reads from or writes to a removable,nonvolatile optical disk 253 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 238 is typically connectedto the system bus 221 through an non-removable memory interface such asinterface 234, and magnetic disk drive 239 and optical disk drive 240are typically connected to the system bus 221 by a removable memoryinterface, such as interface 235.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 4, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 241. In FIG. 4, for example, hard disk drive 238 is illustratedas storing operating system 258, application programs 257, other programmodules 256, and program data 255. Note that these components can eitherbe the same as or different from operating system 225, applicationprograms 226, other program modules 227, and program data 228. Operatingsystem 258, application programs 257, other program modules 256, andprogram data 255 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation into the computer 241 through input devices such as akeyboard 251 and pointing device 252, commonly referred to as a mouse,trackball or touch pad. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit259 through a user input interface 236 that is coupled to the systembus, but may be connected by other interface and bus structures, such asa parallel port, game port or a universal serial bus (USB). The cameras26, 28 and capture device 20 may define additional input devices for theconsole 100. A monitor 242 or other type of display device is alsoconnected to the system bus 221 via an interface, such as a videointerface 232. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 244 and printer 243,which may be connected through a output peripheral interface 233.

The computer 241 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer246. The remote computer 246 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 241, although only a memory storage device 247 has beenillustrated in FIG. 4. The logical connections depicted in FIG. 2include a local area network (LAN) 245 and a wide area network (WAN)249, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 241 is connectedto the LAN 245 through a network interface or adapter 237. When used ina WAN networking environment, the computer 241 typically includes amodem 250 or other means for establishing communications over the WAN249, such as the Internet. The modem 250, which may be internal orexternal, may be connected to the system bus 221 via the user inputinterface 236, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 241, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 4 illustrates remoteapplication programs 248 as residing on memory device 247. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

FIG. 5 depicts a flow diagram of an example method 500 for processingdepth information including, for example, segmenting a human target froman environment in depth image that may be captured by a capture device.The example method 500 may be implemented using, for example, thecapture device 20 and/or the computing environment 12 of the targetrecognition, analysis, and tracking system 10 described with respect toFIGS. 1A-4. In an example embodiment, the example method 500 may takethe form of program code (i.e., instructions) that may be executed by,for example, the capture device 20 and/or the computing environment 12of the target recognition, analysis, and tracking system 10 describedwith respect to FIGS. 1A-4.

According to one embodiment, at 510, a depth image may be received. Forexample, the target recognition, analysis, and tracking system mayinclude a capture device such as the capture device 20 described abovewith respect to FIGS. 1A-2. The capture device 20 may capture or observea scene that may include one or more targets or objects. In an exampleembodiment, the capture device 20 may be a depth camera configured toobtain a depth image of the scene using any suitable technique such astime-of-flight analysis, structured light analysis, stereo visionanalysis, or the like.

The depth image may be a plurality of observed pixels where eachobserved pixel has an observed depth value. For example, the depth imagemay include a two-dimensional (2-D) pixel area of the captured scenewhere each pixel in the 2-D pixel area may have a depth value such as alength or distance in, for example, centimeters, millimeters, or thelike of an object in the captured scene from the capture device.

FIG. 6A illustrates an example embodiment of a depth image 600 that maybe received at 510. According to an example embodiment, the depth image600 may be an image or frame of a scene captured by, for example, the3-D camera 26 and/or the RGB camera 28 of the capture device 20described above with respect to FIG. 2. As shown in FIG. 6A, the depthimage 600 may include one or more human targets 602, 604 correspondingto, for example, one or more users such as the users 18 described abovewith respect to FIGS. 1A and 1B and one or more non-human targets 606such as a wall, a table, a monitor, a couch, a ceiling or the like inthe captured scene. According to an example embodiment, the one or morehuman targets 602, 604 may be players, or other objects that may bedesired to be segmented or separated from an environment tin the depthimage 600 and the one or more non-human targets 606 that may be theenvironment of the depth image 600.

As described above, the depth image 600 may include a plurality ofobserved pixels where each observed pixel has an observed depth valueassociated therewith. For example, the depth image 600 may include atwo-dimensional (2-D) pixel area of the captured scene where each pixelin the 2-D pixel area may represent a depth value such as a length ordistance in, for example, centimeters, millimeters, or the like of atarget or object in the captured scene from the capture device 20. Inone embodiment, the first depth image 600 may be colorized such thatdifferent colors of the pixels of the depth image correspond to and/orvisually depict different distances of the one or more human targets602, 604 and non-human targets 606 from the capture device 20. Forexample, according to one embodiment, the pixels associated with atarget closest to the capture device may be colored with shades of redand/or orange in the depth image whereas the pixels associated with atarget further away may be colored with shades of green and/or blue inthe depth image.

Referring back to FIG. 5, in one embodiment, upon receiving the image,at 505, the image may be downsampled to a lower processing resolutionsuch that the depth image may be more easily used and/or more quicklyprocessed with less computing overhead. Additionally, one or morehigh-variance and/or noisy depth values may be removed and/or smoothedfrom the depth image; portions of missing and/or removed depth valuesmay be filled in and/or reconstructed; and/or any other suitableprocessing may be performed on the received depth image may such thatthe depth image may be processed to, for example, generate a model of ahuman target and track the model of the human target, which will bedescribed in more detail below.

For example, in one embodiment, the target recognition, analysis, andtracking system may calculate portions of missing and/or removed depthvalues for pixels associated with infrared shadows in the depth imagereceived at 505.

FIG. 7 illustrates an example embodiment of a depth image 700 that mayinclude an infrared shadow. The depth image 700 may include a humantarget 602 associated with, for example, the user 18 described abovewith respect to FIGS. 1A and 1B. As shown in FIG. 7, a right hand 702and a left hand 705 may be extended in front of a portion of the humantarget 602.

According to an example embodiment, the right hand 702 and the left hand705 that may be extended in front of a portion of the human target 602may generate respective first and second infrared shadows 708 and 710.The first and second infrared shadow 708 and 710 may include portions ofthe depth image 700 observed or captured by a capture device such as thecapture device 20 described above with respect to FIGS. 1A-2 where abody part may cast a shadow on the scene. According to an exampleembodiment, the capture device may observe or capture an invalid depthvalue such as a depth value of zero for the pixels associated with theportions in the depth image where a body part may cast a shadow on thescene.

The first and second infrared shadows 708 and 710 may separate a bodypart from another body part of human target 602. For example, as shownin FIG. 7, the first infrared shadow 708 may separate the right hand 702from, for example, the right arm of the human target 602. According toan example embodiment, the first and second infrared shadows 708 and 710may separate body parts with invalid depth values. For example, thepixels associated with the portions of first and second infrared shadows708 and 710 may have an invalid depth value. The invalid depth valuesmay separate a body part such as the right hand 702 from, for example, aright arm of the human target 602 that may have pixels with valid,non-zero depth value.

In one embodiment, depth values of the pixels associated with aninfrared shadow such as the infrared shadow 708 may be replaced. Forexample, the target recognition, analysis, and tracking system mayestimate one or more depth values for the shadow that may replace theinvalid depth values. According to one embodiment, the depth value foran infrared shadow pixel may be estimated based on neighboringnon-shadow pixels. For example, the target recognition, analysis, andtracking system may identify an infrared shadow pixel. Upon identifyingthe infrared shadow pixel, the target recognition, analysis, andtracking system may determine whether one or more pixels adjacent to theinfrared shadow pixel may have valid depth values. If one or more pixelsadjacent to the infrared shadow pixel may have valid depth values, adepth value for the infrared shadow pixel may be generated based on thevalid depth values of the adjacent pixels. For example, in oneembodiment, the target recognition, analysis, and tracking system mayestimate or interpolate valid depth values of pixels adjacent to theshadow pixel. The target recognition, analysis, and tracking system mayalso assign the shadow pixel a depth value of one of the adjacent pixelsthat may have a valid depth value.

According to one embodiment, the target recognition, analysis, andtracking system may identify other infrared shadow pixels and calculatedepth values for those pixels as described above until each of theinfrared shadow pixels may have a depth value associated therewith.Thus, in an example embodiment, the target recognition, analysis, andtracking system may interpolate a value for each of the infrared shadowpixels based on neighboring or adjacent pixels that may have a validdepth value associated therewith.

Additionally, in another example embodiment, the target recognition,analysis, and tracking system may calculate depth values for one or moreinfrared shadow pixels based on the depth image of a previous frame. Asdescribed above, the capture device such as the capture device 20described above with respect to FIGS. 1A-2 may capture a scene inframes. Each frame may include a depth image. For example, the systemmay determine whether the corresponding pixel of a previous frame has avalid depth value. Based on the determination, the system may replacethe depth value of the infrared shadow pixel in present depth image withthe depth value of the corresponding pixel of the previous frame.

At 515, a human target in a depth image may be scanned for one or morebody parts. For example, upon receiving a depth image, the targetrecognition, analysis, and tracking system may determine whether thedepth image includes a human target such as the human targets 602 and604 described above with respect to FIG. 6A corresponding to, forexample, a user such as the user 18, described above with respect toFIGS. 1A and 1B. In one embodiment, to determine whether the depth imageincludes a human target, the target recognition, analysis, and trackingsystem may flood fill each target or object in the depth image and maycompare each flood filled target or object to a pattern associated witha body model of a human in various positions or poses. The flood filledtarget, or the human target, that matches the pattern may then bescanned to determine values including, for example, locations and/ormeasurements such as length, width, or the like associated with one ormore body parts. For example, the flood filled target, or the humantarget, that matches the pattern may be isolated and a mask such as abinary mask of the human target may be created. The mask may be createdby, for example, flood filling the human target such that the humantarget may be separated from other targets or objects in the sceneelements. The mask may then be analyzed to determine the locationsand/or measurements for one or more body parts. According to oneembodiment, a model such as a skeletal model, a mesh human model, or thelike of the human target may be generated based on the locations and/ormeasurements for the one or more body parts.

In one embodiment, the target recognition, analysis, and tracking systemmay determine whether a human target in the depth image may have beenpreviously scanned, at 510, before the human target may be scanned at515. For example, the capture device such as the capture device 20described above with respect to FIGS. 1A-2 may capture a scene inframes. Each frame may include a depth image. The depth image of eachframe may be analyzed to determine whether the depth image may include ahuman target as described above. The depth image of each frame mayfurther be analyzed to determine whether the human target may have beenpreviously scanned for one or more body parts. For example, at 510, thetarget recognition, analysis, and tracking system may determine whethera human target in the depth image received, at 505, corresponds to ahuman target previously scanned at 515. In one embodiment, at 510, ifthe human target may not correspond to a human target previouslyscanned, the human target may then be scanned at 515. Thus, according toan example embodiment, a human target may be scanned once in an initialframe and initial depth image captured by the capture device thatincludes the human target. According to another embodiment, the targetrecognition, analysis, and tracking system may scan the human target forone or more body parts in each received depth image that includes thehuman target. The scan results associated with, for example, themeasurements for the one or more body parts may then be averaged.

At 520, an environment of the depth image may be determined. Forexample, as described above, the depth image may be a plurality ofobserved pixels in a two-dimensional (2-D) pixel area where eachobserved pixel has an observed depth value. In one embodiment, thetarget recognition, analysis, and tracking system may determine whetherone or more of the pixels in the depth image may be associated with thehuman target or environment of the depth image. As described above, theenvironment of the depth image may include, for example, environmentobjects behind a human target, environment objects above a human target,environment objects surrounding a left and a right side of a humantarget, environment objects in front of a human target, or the like inthe depth image.

In an example embodiment, the target recognition, analysis, and trackingsystem may determine the environment of the depth image by initiallydefining a bounding box around each foreground object such as each humantarget in the depth image received at 505. For example, the targetrecognition, analysis, and tracking system may define a bounding box foreach human target such as the human targets 602, 604 described abovewith respect to FIG. 6A in the received depth image. According to anexample embodiment, the bounding box may be defined based on a centroidand/or body measurement associated with the human target. For example,as described above, at 515, the target recognition, analysis, andtracking system may scan a human target in a received depth image forone or more body parts. The bounding box may be defined by the centroidand/or measurements determined based on, for example, the scan at 515.After defining the bounding box for each human target, the pixels in thedepth image outside the bounding box may be identified as environment.

FIG. 8 illustrates an example embodiment of a depth image 800 with abounding box 802 defined around a human target. As shown in FIG. 8, thebounding box 802 may be defined by a first side 804 a, a second side 804b, a third side 804 c, and a fourth side 804 d. In one embodiment, thefirst, second, third, and fourth sides 804 a-804 d of the bounding box702 may be calculated based on a centroid and/or one or more bodymeasurements of the human target 602. In one embodiment, the centroid ofthe human target 602 may be calculated based on the scan describedabove. For example, the centroid may be a representation of a joint ornode of, for example, geometric center of the human target. According toan example embodiment, one or more body parts determined by the scan maybe used to calculate the centroid. For example, coordinates of everypixel in a depth image having a threshold probability that the pixel maybe associated with a body part may be averaged to calculate thecentroid. Alternatively, the centroid may be determined based on alinear regression of the measurements and/or locations of a body partdetermined by the scan.

The body measurements such as the length, width, or the like associatedwith one or more body parts and the calculated centroid may then be usedto determine the sides of the bounding box 802. For example, thebounding box 802 may be defined by the intersection of the respectivefirst, second, third, and fourth sides 804 a-804 d. According to anexample embodiment, the location of the first side 804 a and the thirdside 804 c may be determined by adding the measurements such as thelength associated with the respective left and right arms determined bythe scan to an X value associated with the centroid in a direction ofthe left arm and a direction of the right arm. Additionally, in oneembodiment, the location second side 804 b and the fourth side 804 d maybe determined based on the Y value associated with the location of thetop of the head of the human target and the bottom of the legsdetermined by on the scan. The bounding box 802 may then be defined bythe intersection of, for example, the first side 804 a and the secondside 804 b, the first side 804 a and the fourth side 804 d, the thirdside 804 c and the second side 804 b, and the third side 804 c and thefourth side 804 d.

According to an example embodiment, after defining the bounding box forthe human target 602, the pixels in the depth image 800 outside thebounding box 802 may be identified as the non-human target pixel, or thepixels associated with the environment, of the depth image 800.

Referring back to FIG. 5, in one embodiment, the target recognition,analysis, and tracking system may further determine the environment of adepth image by flood filling one or more pixels associated with the ahuman target such as the human target at 520. For example, the targetrecognition, analysis, and tracking system may detect edges of, forexample, the foreground object such as the human target by comparingvarious depth values of nearby pixels such that the pixels within theedges of the human target may be flood filled.

According to an example embodiment, the target recognition, analysis,and tracking system may detect edges of the foreground object such asthe human target by comparing various depth values of nearby pixels thatmay be within the bounding box such as the bounding box 802 describedabove with respect to FIG. 8. For example, as described above, abounding box may be defined around the human target. The pixels outsidethe bounding box may be identified as the environment of the depthimage. The target analysis, recognition, and tracking system may thenanalyze the pixels within the bounding box to determine whether a pixelmay be associated with the human target or the environment such that theedges of the foreground object may be detected.

In one embodiment, the target recognition, analysis, and tracking systemmay further select a predetermined number of sample points as startingpoints to analyze the pixels within the bounding box to determinewhether the pixel may be associated with the human target or theenvironment. For example, the target recognition, analysis, and trackingsystem may randomly select one or more sample points within the boundingbox. In one embodiment, the pixels associated with the randomly selectedsample points may be reference pixels that may be used to initiallycompare pixels to detect edges of the foreground object such as thehuman target, which will be described in more detail below.

FIG. 9B illustrate an example embodiment of a depth image 1000 that mayhave one or more separated body parts and a predefined number of samplepoints 902 selected within, for example, a bounding box. In an exampleembodiment, the sample points 902 may be selected based on the centroidof the human target 602. For example, as shown in FIG. 9B, the targetrecognition, analysis, and tracking system may randomly select, forexample, the sample points 902. The sample points 902 may include 16sample points that may be at various locations that surround thecentroid of the human target 602.

According to another embodiment, the various locations of the samplepoints 902 may be randomly selected using, for example, a shape. Forexample, a shape such as a diamond shape may be used to randomly selectthe sample points 902. The various locations along, for example, theshape such as the diamond shape may be selected as the sample points902.

Additionally, the various locations of the sample points 902 may bebased on, for example, one or more body parts of the human target 602determined by the scan. For example, the various locations of the samplepoints 902 may be selected based on the shoulder width, the body length,the arm length, or the like of the human target. Additionally, thesample points 902 may be selected to cover, for example, the upper body,the lower body, or a combination of the upper and lower body of thehuman target 602.

Referring back to FIG. 5, the target recognition, analysis, and trackingsystem may detect the edges of a human target such as the human targets602, 604 described above with respect to FIGS. 6A and 9 at 520.According to an example embodiment, the target recognition, analysis,and tracking system may detect the edges of a human target by analyzingvarious pixels within, for example, the bounding box using apredetermined edge tolerance. As described above, in one embodiment, thetarget recognition, analysis, and tracking system may select apredetermined number of sample points as starting points to detect theedges of the human target using the predetermined edge tolerance. Usingthe pixels associated with the sample points as an initial reference,the edges may be determined by comparing various depth values associatedwith adjacent or nearby pixels of pixels to detect the edges of thehuman target. Thus, according to an example embodiment, each pixelstarting with, for example, the pixels associated with the sample pointsmay be compared to adjacent or nearby pixels to detect an edge of thehuman target using the predetermined edge or dynamically calculated edgetolerance.

According to an example embodiment, if the various depth values beingcompared may be greater than a predetermined edge tolerance, the pixelsmay define an edge. In one embodiment, the predetermined edge tolerancemay be, for example, 100 millimeters. If a pixel representing a depthvalue of 1000 millimeters may be compared with an adjacent pixelrepresenting a depth value of 1200 millimeters, the pixels may define anedge of a human target such as the human targets 602, 604, because thedifference in the length or distance between the pixels may be greaterthan the predetermined edge tolerance of 100 mm.

According to an example embodiment, the edge tolerance value may varybetween pixels. For example, for pixels in front of a chest of the humantarget, a higher tolerance value may be used to detect the edge of thehuman target. For example, the human target may hold his/her arms infront of the his/her chest. To accurately detect the edges of the handsof the human target 602, the target recognition, analysis, and trackingsystem may use a higher tolerance value. In another example, the humantargets may extend his/her arms away from his/her torso. In thisexample, the target recognition, analysis, and tracking system may use alower tolerance value to detect the edges of the human target's hands.According to one embodiment, the variable edge tolerance may bedetermined based on, for example, a location of the pixel, a length ofan arm of the human target, and/or a width of the shoulder of the humantarget. According to another example embodiment, the variable edgetolerance may be interpolated such that the detected edge may be asmooth curve.

In one embodiment, the pixels within the detected edges of the humantargets may be flood filled to isolate and/or identify the human targetsuch as the human targets 602, 604. The pixels that may not be floodfilled may then be identified or associated with the environment of thedepth image such that the pixels may be removed, which will be describedin more detail below.

According to an example embodiment, one body part of the human target602 may be separated from another body part of the human body. Forexample, as described above with respect to FIG. 7, an infrared shadowmay be cast by a body part such that the body part may be separated fromanother body part of the human target. In another example embodiment, abody part such as a head may be separated from a torso of the humantarget by, for example, facial hair, various articles of clothing, orthe like.

Additionally, as described above, the body parts that may be separatedby, for example, facial hair, various articles of clothing, or the likeby invalid depth values. For example, the capture device such as thecapture device 20 described above with respect to FIGS. 1A-2 may captureor observe an invalid depth value such as a zero or an invalid depthvalue for one or more pixels associated with facial hair, variousarticles of clothing, or the like. As described above, in oneembodiment, the target recognition, analysis, and tracking system mayestimate valid depth values for one or more of the pixels associatedwith facial hair, various articles of clothing, or the. After estimatingvalid depth values, the body parts may still be separated. For example,the target recognition, analysis, and tracking system may not be able toestimate a valid depth value for each of the pixels of the facial hair,various articles of clothing, or the like. According to an exampleembodiment, the target recognition, analysis, and tracking system maydetermine the environment of the depth image with the invalid depthvalues for those pixels.

FIGS. 9A-9B illustrate an example embodiment of a depth image 1000 thatmay have one or more separated body parts. As shown in FIG. 9A, thedepth image 1000 may include the human target 602. In an exampleembodiment, the human target 602 may have a head 1002, a beard 1005, anda torso 1007. As shown in FIG. 9A, the head 1002 may be separated fromthe torso 1007 by the beard 1005. According to an example embodiment,the target recognition, analysis, and tracking system may usemulti-sample flood filling with randomly selected sample points asdescribed above to identify and flood fill one or more isolated bodyparts such as the head 1002 of the human target 602.

As shown in FIG. 9B, both the head 1002 and the torso 1007 may be floodfilled as pixels associated with the human target 602, despite the beard1005 separating the head 1002 from the torso 1007. For example, asdescribed above, the target recognition, analysis, and tracking systemmay randomly generate a predefined number of sample points 802 in, forexample, diamond shape around a centroid of the human target 602. Asshown in FIG. 9B, three of the sample points 902 may be associated withpixels of the head 1002. In view of these three sample points, thetarget recognition, analysis, and tracking system may determine that thepixels associated with head 1002 belong to the head of the human target602, and accordingly, flood fill the isolated head 1002 as targetpixels.

As described above, each sample point may serve as the starting pointsto determine whether pixels are associated with a human target such thatthe human target may be flood filled. For example, the targetrecognition, analysis, and tracking system may start flood filling at afirst sample point that may be the centroid of a human target 602.Thereafter, the target recognition, analysis, and tracking system maypick a second sample point to determine whether pixels are associatedwith the human target 602.

In an example embodiment, the target recognition, analysis, and trackingsystem may examine the depth value of the each of the sample points. Forexample, if the depth value of the second sample point may be close orwithin a predetermined tolerance to the depth value of the centroid ofthe human target 602, the target recognition, analysis, and trackingsystem may identify the sample point as being associated with anisolated body part of the human target 602. As described above,according to one example embodiment, the predefined tolerance may bedetermined based on values including, but not limited to, locationsand/or measurements such as length, width, or the like associated withone or more body parts. Thus, according to an example embodiment, thetarget recognition, analysis, and tracking system may use the samplepoints as starting points to determine whether pixels are associatedwith a human target such that the pixels may be flood filled.

Referring back to FIG. 5, at 520, the target recognition, analysis, andtracking system may use depth history data to determine the environmentor the non-human target pixels of the depth image. For example, thetarget recognition, analysis, and tracking system may determine whethera pixel may be associated with a human target by flood filling asdescribed above. If the pixel may not be associated with the humantarget based on flood filling, the target recognition, analysis, andtracking system may discard the pixel as part of the environment. If thepixel appears to be a pixel associated with the human target, the targetrecognition, analysis, and tracking system may analyze the pixel withrespect to depth history data, including, for example, the historicalmaximum depth value of the pixel, average or standard deviation. Forexample, as described above, the capture device such as the capturedevice 20 described above with respect to FIGS. 1A-2 may capture a scenein frames. Each frame may include a depth image. The depth image of eachframe such as the depth image 600 as shown in FIG. 6A may be analyzed toextract and/or store a historical maximum depth value for each pixeltherein.

FIG. 10 depicts an example embodiment of depth history data 1100. Themaximum depth values may be representations of a distance of, forexample, a wall 1140, a couch 1145, an estimated depth value behind aleg 1150, a ceiling 1155, a floor 1160, or any other objects that may becaptured by the capture device 20. According to an example embodiment,the depth history data may capture depth values of one or more objectsassociated with the environment of the depth image. Thus, in oneembodiment, the depth history data may capture or observe depth valuesof the environment as if capturing a scene with human targets 602removed from the view of the capture device 20.

According to one embodiment, the maximum depth value of a pixel in depthhistory data may be estimated. For example, as shown in FIG. 11A, a usersuch as the user 18 described above with respect to FIG. 1A-2 may standin front of a wall. The target recognition, analysis, and tracking maycapture or observe the human target 602 in the depth image 800 that maybe associated with the user 18. The target recognition, analysis, andtracking system may scan the human target 602 as described above to findthe location of the human target. The target recognition, analysis, andtracking system may then estimate the depth values of the pixelsassociated with the wall behind the location of the human target 602determined by the scan such that the estimated depth values may beincluded in the depth history data. For example, the target recognition,analysis, and tracking system may record and/or store the depth value ofthe wall 840 captured by the capture device 20 as the maximum depthvalues of the pixels associated the wall. The target recognition,analysis, and tracking system may then estimate the depth values wallpixels covered by human target 602 based on the depth values of one ormore surrounding pixels associated with the wall. Thus, according to anexample embodiment, the target recognition, analysis, and trackingsystem may gather and/or analyze information such as depth values of oneor more objects surrounding human target 602 to accurately remove theenvironment of a depth image.

According to one embodiment, the maximum depth values in the depthhistory data may be updated as the capture device such as the capturedevice 20 observes or captures depth images from frame to frame. Forexample, in a first frame, the depth image may capture a human target602 on the left half of the frame, and environment objects on the righthalf of the frame may be exposed to the camera. The maximum depth valuesin the depth history data may be updated to reflect the depth values ofpixels associated with environment objects on the right side of theframe. For example, when the human target 602 moves to the right half ofthe frame, environment objects on the left hand side of the frame may beexposed to the capture device 20. The maximum depth values in the depthhistory data may be updated to reflect the depth values of pixelsassociated with environment objects on the left half of the camera view.In other words, as the human target moves from frame to frame, aenvironment object may be visible to the capture device such that thedepth history data may be updated to reflect the depth values of pixelsassociated with the environment object.

In example embodiments, the target recognition, analysis, and trackingsystem may update the maximum depth values for a subset of pixels ineach frame. For example, a frame may include a predefined number of scanlines scan lines or the like. In one embodiment, the target recognition,analysis, and tracking system may update the maximum depth values forpixels on one horizontal scan line per frame in an top to bottomdirection, by temporal averaging or other suitable mechanism of updatingthe history pixels over multiple frames. In other embodiments, thesystem may update the maximum depth values of pixels, in a bottom to topdirection, or update one vertical scan line per frame, in a left toright direction, or in a right to left direction, or the like.Accordingly, the maximum depth values of a frame may be updatedgradually to keep to track of objects in the camera view.

According to an example embodiment, a depth value of the pixel beingexamined may be compared to the maximum depth value of the pixel basedon the depth history data. For example, if the pixel being exampled mayhave the same depth value as the historical maximum depth value of thepixel, the target recognition, analysis, and tracking system maydetermine that the pixel may be associated with the environment of thedepth image. Alternatively, in one embodiment, if the depth value of thepixel being examined may be less than the historical maximum depth valueof the pixel within, for example, a predetermined tolerance valuedescribed above, the target recognition, analysis, and tracking systemmay determine that the pixel may be associated with a foreground objectsuch as the human target and the pixel may then be flood filled. Thus,according to an example embodiment, the depth history data may be usedto confirm that a pixel may be associated with a human target.

FIG. 11A depicts an example embodiment of a depth image that may becaptured. The depth image may include a human target 602 touching a wall840. According to an example embodiment, the hand of the human target602 and the wall 840 may have the similar depth values as shown in thearea within a portion 810 of the depth image. In an example embodiment,if the difference depth values between the hand and the wall 840 may besmall enough, or less that a predetermined tolerance, the targetrecognition, analysis, and tracking may not be able to detect an edge ofthe hand. Thus, in an example, embodiment, the target recognition,analysis, and tracking may use the depth history data to determinewhether a pixel in the portion 810 may be associated with theenvironment or the human target 602.

FIG. 11B depicts an example embodiment of depth history data that mayinclude maximum depth values accumulated over a number of frames. Asdiscussed above, depth history data may provide an estimate of the depthvalues of the environment surrounding the human target. As shown in FIG.11B, the depth history data may include depth values of the pixelsassociated with a wall 840. For example, the area within a portion 820may capture the depth values of pixels associated with the wall 840,which may be covered by the hand of the human target 602 in the depthimage 800 in FIG. 11A. According to one embodiment, the targetrecognition, analysis, and tracking system may compare a depth value ofa pixel within the portion 810 of FIG. 11A with a maximum depth value inthe depth history data of a corresponding pixel in a portion 820 of FIG.11B. If the depth value of the pixel in the portion 810 may have thesame value as the historical maximum depth value of the pixel in theportion 820, the target recognition, analysis, and tracking system maydetermine that the pixel may be associated with the wall 840. The pixelsin the portion 810 associated with the wall 840 may then be discarded asthe environment as shown in FIG. 11C.

Alternatively, if the depth value of a pixel in the portion 810 may beless than the historical maximum depth value of the pixel in the portion1020, the target recognition, analysis, and tracking system maydetermine that the pixel may be associated with the human target 602such that the pixel may be flood filled.

According to one embodiment, the target, recognition, analysis, andtracking system may check the depth history data when an edge having asmall predetermined tolerance value may be detected. For example, thetarget, recognition, analysis, and tracking system may determine whetherthe depth difference between two pixels that may define an edge may bewithin a predetermined tolerance value. If the depth difference may beless than the predetermined value, the target, recognition, analysis,and tracking system may proceed to access the depth history data. In anexample embodiment, the tolerance value may be predetermined based onnoise in the depth image received, captured, or observed by the capturedevice such as the capture device 20 shown in FIGS. 1A-2. The tolerancevalue may also vary depending on the type of capture devices, the depthvalues, or the like. That is, according to one embodiment, the tolerancevalue may be larger or smaller as the depth value of a pixel increases.

The depth history data may further include floor pixels. For example,the difference between dept values associated with feet of a humantarget such as the human target 602 and the floor may be within a smallpredetermined tolerance or value similar to when a hand of the humantarget may touch a wall as described above. The target, recognition,analysis, and tracking the system may further track the depth values ofpixels associated with the floor in depth history data. For example, thedepth values of the floor may be detected and stored or recorded intothe depth history data. When examining a pixel in the floor area, thetarget, recognition, analysis, and tracking system may compare the depthvalue of the pixel being examined with the corresponding floor pixel indepth history data.

Referring back to FIG. 5, at 525, the environment of the depth image maybe removed or discarded. For example, upon flood filling the pixelsassociated with the human target by determining whether pixels may beassociated with the human target as described above, the targetrecognition, analysis and tracking system may discard the pixels thatmay not be associated with the flood filled human target. Thus, in oneembodiment, at 525, the target recognition analysis and tracking systemmay discard or remove the pixels associated with the environment of thedepth image based on the flood filled human target such that the humantarget including the pixels and depth values associated therewith may beisolated in the depth image. According to an example embodiment, thetarget recognition, analysis, and tracking system may discard the pixelsassociated with the environment by assigning them, for example, aninvalid depth value such as a depth value of zero.

FIGS. 6B, 9B, and 11C illustrate example embodiments of a depth imagewith the environment removed. As shown in FIGS. 6B, 9B, and 11C thehuman target such as the human targets 602, 604 may be isolated in thedepth images.

Referring back to FIG. 5, the depth image with the isolated human targetmay be processed at 530. In one embodiment, the target recognition,analysis, and tracking system may process the depth image with theisolated human target such that a model of the human target in thecaptured scene may be generated. According to an example embodiment, themodel may be tracked, an avatar associated with the model may berendered, and/or one or more applications executing on a computerenvironment may be controlled.

For example, according to an example embodiment, a model such as askeletal model, a mesh human model, or the like of a user such as theuser 18 described above with respect to FIGS. 1A and 1B may generatedand tracked for one or more movements by the user.

The visual appearance of an on-screen character may then be changed inresponse to changes to the model being tracked. For example, a user suchas the user 18 described above with respect to FIGS. 1A and 1B playingan electronic game on a gaming console may be tracked by the gamingconsole as described herein. In particular, a body model such as askeletal model may be used to model the target game player, and the bodymodel may be used to render an on-screen player avatar. As the gameplayer straightens one arm, the gaming console may track this motion,then in response to the tracked motion, adjust the body modelaccordingly. The gaming console may also apply one or more constraintsto movements of the body model. Upon making such adjustments andapplying such constraints, the gaming console may display the adjustedplayer avatar.

In one embodiment, the target recognition, analysis, and tracking systemmay not be able to process the second depth image at 530. For example,the depth image may be too noisy or include too many empty pixels suchthat the depth image may not be processed. According to one embodiment,if the depth values may be too noisy, the target recognition, analysis,and tracking system may generate an error message that may be providedto a user such as the user 18 described above with respect to FIGS. 1Aand 1B to indicate that another scene may need to be captured.

It should be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered limiting. The specificroutines or methods described herein may represent one or more of anynumber of processing strategies. As such, various acts illustrated maybe performed in the sequence illustrated, in other sequences, inparallel, or the like. Likewise, the order of the above-describedprocesses may be changed.

The subject matter of the present disclosure includes all novel andnonobvious combinations and subcombinations of the various processes,systems and configurations, and other features, functions, acts, and/orproperties disclosed herein, as well as any and all equivalents thereof.

What is claimed:
 1. A system, comprising: a processor; and a memorycommunicatively coupled to the processor when the device is operational,the memory bearing processor-executable instructions that, when executedupon the processor, cause the system to at least: receive a depth imageof the scene; determine a centroid of a target in the scene; define abounding box for the target based on a centroid of the target and ameasurement of the target; and identify a pixel of the depth image thatis outside of the bounding box as being associated with an environmentof the scene.
 2. The system of claim 1, wherein the memory further bearsinstructions that, when executed upon the processor, cause the system toat least: define the bounding box for the target from depth historydata.
 3. The system of claim 2, wherein the memory further bearsinstructions that, when executed upon the processor, cause the system toat least: track a maximum depth value for the pixel of the depth image;and store the maximum depth value in historic depth value data.
 4. Thesystem of claim 3, wherein the memory further bears instructions that,when executed upon the processor, cause the system to at least: identifya pixel associated with a floor in the scene; and store a depth value ofthe pixel associated with the floor in the historic depth value data. 5.The system of claim 1, wherein the instructions that, when executed uponthe processor, cause the system to at least define the bounding box forthe target further cause the system to at least: flood-fill a pixelwithin the bounding box for the target.
 6. The system of claim 5,wherein the instructions that, when executed upon the processor, causethe system to at least define the bounding box for the target furthercause the system to at least: define the bounding box for the targetusing a predetermined edge tolerance value.
 7. The system of claim 6,wherein the instructions that, when executed upon the processor, causethe system to at least define the bounding box for the target using thepredetermined edge tolerance value further cause the system to at least:adjust the predetermined edge tolerance value based on a measurement ofthe target, or a position of the first pixel.
 8. The system of claim 1,wherein the instructions that, when executed upon the processor, causethe system to at least identify a pixel of the depth image that isoutside of the bounding box as being associated with an environment ofthe scene further cause the system to at least: flood-fill the pixel ofthe depth image that is outside of the bounding box.
 9. The system ofclaim 1, wherein the memory further bears instructions that, whenexecuted upon the processor, cause the system to at least: identify apixel associated with an infrared shadow in the depth image; and replacea depth value of the pixel associated with the infrared shadow with acalculated depth value.
 10. The system of claim 1, wherein the memoryfurther bears instructions that, when executed upon the processor, causethe system to at least: remove the pixel of the depth image that isoutside of the bounding box in response to determining that the pixel isoutside of the bounding box.
 11. A method, comprising: receiving a depthimage of the scene; determining a centroid of a target in the scene;defining a bounding box for the target based on a centroid of the targetand a measurement of the target; and identifying a pixel of the depthimage that is outside of the bounding box as being associated with anenvironment of the scene.
 12. The method of claim 11, furthercomprising: defining the bounding box for the target from depth historydata.
 13. The method of claim 11, further comprising: tracking a maximumdepth value for the pixel of the depth image; and storing the maximumdepth value in historic depth value data.
 14. The method of claim 13,further comprising: identifying a pixel associated with a floor in thescene; and storing a depth value of the pixel associated with the floorin the historic depth value data.
 15. A computer-readable storagemedium, bearing computer-readable instructions that, when executed on acomputer, cause the computer to perform comprising: receiving a depthimage of the scene; determining a centroid of a target in the scene;defining a bounding box for the target based on a centroid of the targetand a measurement of the target; and identifying a pixel of the depthimage that is outside of the bounding box as being associated with anenvironment of the scene.
 16. The computer-readable storage medium ofclaim 15, wherein defining the bounding box for the target comprises:flood-filling a pixel within the bounding box for the target.
 17. Thecomputer-readable storage medium of claim 16, wherein defining thebounding box for the target comprises: defining the bounding box for thetarget using a predetermined edge tolerance value.
 18. Thecomputer-readable storage medium of claim 17, wherein defining thebounding box for the target using the predetermined edge tolerance valuecomprises: adjusting the predetermined edge tolerance value based on ameasurement of the target, or a position of the first pixel.
 19. Thecomputer-readable storage medium of claim 15, wherein identifying apixel of the depth image that is outside of the bounding box as beingassociated with an environment of the scene comprises: flood-filling thepixel of the depth image that is outside of the bounding box.
 20. Thecomputer-readable storage medium of claim 15, further bearingcomputer-readable instructions that, when executed on the computer,cause the computer to perform comprising: identify a pixel associatedwith an infrared shadow in the depth image; and replacing a depth valueof the pixel associated with the infrared shadow with a calculated depthvalue.